scholarly journals Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1670 ◽  
Author(s):  
Danchen Zhao ◽  
Yaochen Li ◽  
Yuehu Liu
Keyword(s):  
Driving Behavior ◽  
Unmanned Vehicles ◽  
Sensor Data ◽  
The Road ◽  
Driving Behaviors ◽  
Slowing Down ◽  
3D Space ◽  
Geometric Consistency ◽  
Virtual Vehicle ◽  
Main Basis

Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle’s image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect.

scholarly journals A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1356 ◽  
Author(s):  
Jun Zhang ◽  
ZhongCheng Wu ◽  
Fang Li ◽  
Chengjun Xie ◽  
Tingting Ren ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Can Bus ◽  
Driving Behavior ◽  
Sensor Data ◽  
Area Network ◽  
Feature Representations ◽  
Learning Framework ◽  
Driving Behaviors ◽  
Temporal Features

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

scholarly journals Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Seat Belt ◽  
Driving Behavior ◽  
Video Data ◽  
Older Drivers ◽  
Driving Frequency ◽  
Middle Aged ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Study Results

Abstract Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p<.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

Advance Information on the Road: A Simulator Study of the Effect of Road Markings

1976 ◽  
Vol 18 (6) ◽  
pp. 521-532 ◽  
Author(s):  
Harald Witt ◽  
Carl G. Hoyos
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Speed Profile ◽  
The Road ◽  
Positive Effects ◽  
Road Signs ◽  
Advance Information ◽  
Simulator Study ◽  
On The Road ◽  
Accident Statistics

Accident statistics and studies of driving behavior have shown repeatedly that curved roads are hazardous. It was hypothesized that the safety of curves could be improved by indicating in advance the course of the road in a more effective way than do traditional road signs. A code of sequences of stripes put on right edge of the pavement was developed to indicate to the driver the radius of the curve ahead. The main characteristic of this code was the frequency of transitions from code elements to gaps between elements. The effect of these markings was investigated on a driving simulator. Twelve subjects drove on simulated roads of different curvature and with different placement of the code in the approach zone. Some positive effects of the advance information could be observed. The subjects drove more steadily, more precisely, and with a more suitable speed profile.

Optimization of the characteristic angles of both front and rear McPherson suspensions on a circular track using multi-body numerical simulation

2009 ◽  
Vol 223 (9) ◽  
pp. 1119-1132 ◽  
Author(s):  
G Virzì Mariotti ◽  
G Ficarra
Keyword(s):  
Numerical Simulation ◽  
Contact Forces ◽  
Roll Motion ◽  
The Road ◽  
Virtual Vehicle ◽  
Circular Track ◽  
Characteristic Values ◽  
Optimal Values ◽  
Multi Body

The research reported in this paper aims to simulate the road-holding of a virtual vehicle using multi-body simulation to estimate both the contact forces between the tyre and ground and the roll motion when cornering. Furthermore, the effect of the characteristic angles on the variation in the forces of the tyre in contact with the ground is studied to determine optimal values for these angles. Emphasis is placed on an average-class vehicle, of which both the external dimensions and mass are chosen appropriately, with a McPherson suspension mounted on both the front and the rear. The characteristic values of the camber and toe-in angles, in both the front and the rear, are optimized for motion in the curve under constant traction. The results of numerical simulation are compared with results from the theory of stability in the curve (given the vertical configuration of the vehicle).

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals Driving Performance Evaluation Correlated to Age and Visual Acuities Based on VR Technologies

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sooncheon Hwang ◽  
Sunhoon Kim ◽  
Dongmin Lee
Keyword(s):  
Visual Acuity ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Performance ◽  
Present System ◽  
The South ◽  
Dynamic Visual Acuity ◽  
South Korean ◽  
Driving Behaviors ◽  
License System

There is currently much debate regarding the effectiveness of the driver license system in South Korea, due to the numerous traffic crashes caused by drivers who are suspected of having insufficient physical and mental abilities. Through the present system, it is quite difficult to identify such drivers indirectly through physical tests, such as visual acuity tests, since the correlation of such results with driving performance remains unclear. The objective of this study was to investigate the relationship between driving performance and visual acuities for improving the South Korean driver license system. In this study, two investigations were conducted: static and dynamic visual acuity examinations and driving performance tests based on a virtual reality (VR) system. The driving performance was evaluated with a driving simulator, based on driving behaviors in different experimental scenarios, including daytime and nighttime driving on a rural highway, and unexpected incident situations. Here, we produce statistically significant evidence that reduced visual acuity impairs driving performance, and driving behaviors differ significantly among groups with different vision capabilities, especially dynamic vision. Visual acuities, typically dynamic visual acuity, greatly influenced driving behavior, as measured by the standard deviation of speeds and vehicle LPs, and this was especially notable in curved road segments in daytime experiment. These experimental results revealed that the driving performance of participants with impaired dynamic visual acuity was deficient and unsafe. This confirmed that dynamic visual acuity levels are significant determinants of driving behavior, and they well explain driver performance levels. These findings suggest that the South Korean driver license system should include a test of dynamic visual acuity to create better and safer driving.

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